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On the Trend Recognition and Forecasting Ability of Professional Traders

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  • Glaser, Markus
  • Langer, Thomas
  • Weber, Martin

Abstract

Empirical research documents that temporary trends in stock price movements exist. Moreover, riding a trend can be a profitable investment strategy. Thus, the ability to recognize trends in stock markets influences the quality of investment decisions. In this Paper, we provide a thorough test of the trend recognition and forecasting ability of financial professionals who work in the trading room of a large bank and novices (MBA students). In an experimental study, we analyse two ways of trend prediction: probability estimates and confidence intervals. Subjects observe stock price charts, which are artificially generated by either a process with positive or negative trend and are asked to provide subjective probability estimates for the trend. In addition, the subjects were asked to state confidence intervals for the development of the chart in the future. We find that depending on the type of task either underconfidence (in probability estimates) or overconfidence (in confidence intervals) can be observed in the same trend prediction setting based on the same information. Underconfidence in probability estimates is more pronounced the longer the price history observed by subjects and the higher the discriminability of the price path generating processes. Furthermore, we find that the degree of overconfidence in both tasks is significantly positively correlated for all experimental subjects whereas performance measures are not. Our study has important implications for financial modelling. We argue that the question which psychological bias should be incorporated into a model does not depend on a specific informational setting but solely on the specific task considered. This Paper demonstrates that a theorist has to be careful when deriving assumptions about the behaviour of agents in financial markets from psychological findings.

Suggested Citation

  • Glaser, Markus & Langer, Thomas & Weber, Martin, 2003. "On the Trend Recognition and Forecasting Ability of Professional Traders," CEPR Discussion Papers 3904, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:3904
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    References listed on IDEAS

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    Cited by:

    1. repec:zbw:rwirep:0387 is not listed on IDEAS
    2. Stefan Ruenzi, 2005. "Mutual Fund Growth in Standard and Specialist Market Segments," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 19(2), pages 153-167, August.
    3. Pikulina, Elena & Renneboog, Luc & Tobler, Philippe N., 2017. "Overconfidence and investment: An experimental approach," Journal of Corporate Finance, Elsevier, vol. 43(C), pages 175-192.
    4. Brosig-Koch, Jeannette & Keldenich, Klemens, 2012. "The More You Know? – Consumption Behavior and the Communication of Economic Information," Ruhr Economic Papers 387, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    5. Fong, Wai Mun & Yong, Lawrence H. M., 2005. "Chasing trends: recursive moving average trading rules and internet stocks," Journal of Empirical Finance, Elsevier, vol. 12(1), pages 43-76, January.
    6. Pikulina, E.S. & Renneboog, L.D.R. & Tobler, P.N., 2014. "Overconfidence, Effort, and Investment (Revised version of CentER DP 2013-035)," Discussion Paper 2014-039, Tilburg University, Center for Economic Research.
    7. Menkhoff, Lukas & Schmidt, Ulrich & Brozynski, Torsten, 2006. "The impact of experience on risk taking, overconfidence, and herding of fund managers: Complementary survey evidence," European Economic Review, Elsevier, vol. 50(7), pages 1753-1766, October.
    8. Enrique Fatas & Tibor Neugebauer & Pilar Tamborero, 2007. "How Politicians Make Decisions: A Political Choice Experiment," Journal of Economics, Springer, vol. 92(2), pages 167-196, October.
    9. Fellner-Röhling, Gerlinde & Krügel, Sebastian, 2014. "Judgmental overconfidence and trading activity," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 827-842.
    10. Kourtidis, Dimitrios & Šević, Željko & Chatzoglou, Prodromos, 2011. "Investors’ trading activity: A behavioural perspective and empirical results," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 40(5), pages 548-557.
    11. repec:eco:journ1:2017-02-02 is not listed on IDEAS
    12. Zacharias Sautner & Martin Weber, 2009. "How Do Managers Behave In Stock Option Plans? Clinical Evidence From Exercise And Survey Data," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 32(2), pages 123-155.
    13. Christoph Gort & Mei Wang, 2010. "Overconfidence and Active Management," Chapters,in: Handbook of Behavioral Finance, chapter 12 Edward Elgar Publishing.
    14. Gerlinde Fellner, 2004. "Illusion of control as a source of poor diversification: An experimental approach," Papers on Strategic Interaction 2004-28, Max Planck Institute of Economics, Strategic Interaction Group.
    15. Leitner, Stephan & Rausch, Alexandra & Behrens, Doris A., 2017. "Distributed investment decisions and forecasting errors: An analysis based on a multi-agent simulation model," European Journal of Operational Research, Elsevier, vol. 258(1), pages 279-294.
    16. Jeannette Brosig-Koch & Klemens Keldenich, 2012. "The More You Know? – Consumption Behavior and the Communication of Economic Information," Ruhr Economic Papers 0387, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.

    More about this item

    Keywords

    conservatism; financial modelling; forecasting; overconfidence; professionals; trend recognition;

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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